An implementation of WaveNet with fast generation

Last update: Dec 27, 2022

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Text Data & NLP pytorch-wavenet

Overview

pytorch-wavenet

This is an implementation of the WaveNet architecture, as described in the original paper.

Features

Automatic creation of a dataset (training and validation/test set) from all sound files (.wav, .aiff, .mp3) in a directory
Efficient multithreaded data loading
Logging to TensorBoard (Training loss, validation loss, validation accuracy, parameter and gradient histograms, generated samples)
Fast generation, as introduced here

Requirements

python 3
pytorch 0.3
numpy
librosa
jupyter
tensorflow for TensorBoard logging

Demo

For an introduction on how to use this model, take a look at the WaveNet demo notebook. You can find audio clips generated by a simple trained model in the generated samples directory

Owner

Vincent Herrmann

GitHub Repository

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